read.markets/app/jobs/ai_log_job.py

"""Hourly AI strategic-log generator. Pulls already-persisted market data and
headlines from the DB (no live fetches), calls OpenRouter, persists the log
and a row in the cost ledger."""
from __future__ import annotations

import asyncio

import httpx
from sqlalchemy import desc, func, select

from app.config import get_settings
from app.db import utcnow
from app.jobs._helpers import job_lifecycle, log
from app.jobs._market_context import (
    REFERENCE_LINE,
    latest_quotes_by_group,
    month_spend,
    recent_headlines_by_bucket,
)
from app.models import AICall, JobRun, StrategicLog, StrategicLogTranslation, User
from app.services.cadence import DEFAULT_POLICY
from app.services.i18n import ACTIVE_LANGUAGES
from app.services.llm_prompts import (
    PROMPT_VERSION,
    build_system_prompt,
    build_user_prompt,
)
from app.services.output_review import review_read
from app.services.openrouter import (
    active_model,
    call_llm,
    llm_configured,
)
from app.services.translation import translate


async def translate_log_for_active_languages(session, log_id: int) -> None:
    """Fan out per-language translations for the strategic log identified
    by ``log_id``.

    Reads ``users.lang`` (deduplicated, restricted to ACTIVE_LANGUAGES
    minus English), one translation call per language in parallel via
    ``asyncio.gather``, persists each successful result as a
    ``StrategicLogTranslation`` row. Each row is committed in its own
    savepoint so a per-language LLM error or DB error doesn't roll back
    the languages that already succeeded.

    The job orchestrator calls this AFTER the English ``StrategicLog``
    row is committed; pass the row's ``id`` in.
    """
    target_langs = sorted({l for l in ACTIVE_LANGUAGES if l != "en"})
    if not target_langs:
        return

    active_langs = (await session.execute(
        select(User.lang).distinct().where(User.lang.in_(target_langs))
    )).scalars().all()
    if not active_langs:
        return

    log_row = await session.get(StrategicLog, log_id)
    if log_row is None:
        log.warning("log.translate.missing_log", log_id=log_id)
        return

    async with httpx.AsyncClient(follow_redirects=True, timeout=60) as client:
        results = await asyncio.gather(*[
            translate(client, log_row.content, lang)
            for lang in active_langs
        ], return_exceptions=True)

    succeeded = 0
    failed = 0
    for lang, result in zip(active_langs, results):
        if isinstance(result, Exception):
            log.warning("log.translate.failed", lang=lang, log_id=log_id,
                        error=str(result)[:200])
            failed += 1
            continue
        translated_md, llm_result = result
        try:
            async with session.begin_nested():
                session.add(StrategicLogTranslation(
                    log_id=log_id, lang=lang,
                    content=translated_md,
                    generated_at=utcnow(),
                    model=llm_result.model,
                    prompt_tokens=llm_result.prompt_tokens,
                    completion_tokens=llm_result.completion_tokens,
                    cost_usd=llm_result.cost_usd,
                ))
            await session.commit()
            succeeded += 1
        except Exception as exc:
            log.warning("log.translate.persist_failed",
                        lang=lang, log_id=log_id, error=str(exc)[:200])
            failed += 1

    if failed and succeeded == 0:
        log.error("log.translate.all_failed",
                  log_id=log_id, attempted=len(active_langs))
    else:
        log.info("log.translate.done",
                 log_id=log_id, succeeded=succeeded, failed=failed)


async def run() -> None:
    async with job_lifecycle("ai_log_job") as (session, jr):
        if jr.status == "skipped":
            return
        s = get_settings()
        if not llm_configured():
            log.warning("ai_log.skipped_no_key", provider=s.LLM_PROVIDER)
            jr.status = "skipped"
            return

        # Cadence: hourly during EU/US active hours; throttled off-hours.
        last_success = (await session.execute(
            select(func.max(JobRun.finished_at)).where(
                JobRun.name == "ai_log_job",
                JobRun.status == "success",
            )
        )).scalar()
        should_run, reason = DEFAULT_POLICY.should_run(last_success)
        if not should_run:
            log.info("ai_log.cadence_skip", reason=reason)
            jr.status = "skipped"
            jr.error = reason
            return

        spent = await month_spend(session)
        if spent >= s.OPENROUTER_MONTHLY_CAP_USD:
            log.warning("ai_log.cap_reached", spent=spent,
                        cap=s.OPENROUTER_MONTHLY_CAP_USD)
            jr.status = "skipped"
            jr.error = f"monthly cost cap reached (${spent:.2f})"
            return

        quotes = await latest_quotes_by_group(session)
        news = await recent_headlines_by_bucket(session)
        if not quotes and not news:
            log.warning("ai_log.no_data_yet")
            jr.status = "skipped"
            return

        # Look up the most recent log generated today (UTC) so the model can
        # update it rather than start from scratch. This gives the model
        # temporal awareness — "since this morning's read, X has changed".
        today_start = utcnow().replace(hour=0, minute=0, second=0, microsecond=0)
        previous_log = (await session.execute(
            select(StrategicLog)
            .where(StrategicLog.generated_at >= today_start)
            .order_by(desc(StrategicLog.generated_at))
            .limit(1)
        )).scalar_one_or_none()

        anchor = s.CASSANDRA_ANCHOR_DATE or None
        user_prompt = build_user_prompt(
            today=utcnow(),
            anchor=anchor,
            quotes_by_group=quotes,
            headlines_by_bucket=news,
            reference_line=REFERENCE_LINE,
            previous_log=previous_log,
        )

        # Phase 2 voice pivot (PROMPT_VERSION 6): generate both tones per
        # run so the dashboard toggle is instant. Analysis stays on the
        # operator-configured default (DRY|SPECULATIVE is a system-wide
        # preference, not a per-user toggle). PRO was dropped.
        analysis = (s.CASSANDRA_ANALYSIS or "SPECULATIVE").upper()
        variants = [
            ("NOVICE", analysis),
            ("INTERMEDIATE", analysis),
        ]
        written = 0
        async with httpx.AsyncClient(follow_redirects=True) as client:
            for tone, analysis in variants:
                # Re-check cost cap between variants so a runaway run is
                # bounded.
                spent = await month_spend(session)
                if spent >= s.OPENROUTER_MONTHLY_CAP_USD:
                    log.warning("ai_log.cap_reached_midrun",
                                spent=spent, completed=written)
                    break

                system_prompt = build_system_prompt(tone, analysis)
                try:
                    result = await call_llm(
                        client,
                        [{"role": "system", "content": system_prompt},
                         {"role": "user", "content": user_prompt}],
                    )
                except Exception as e:
                    session.add(AICall(
                        model=active_model(), status="error",
                        error=f"{tone}/{analysis}: {str(e)[:480]}",
                    ))
                    await session.commit()
                    log.error("ai_log.variant_failed",
                              tone=tone, analysis=analysis, error=str(e)[:200])
                    continue

                # Reviewer gate: catches chain-of-thought, truncation,
                # and (regulatory-critical) any financial-advice phrasing
                # that drifted past the generator's system prompt. Drop
                # rejected variants; the API falls back to the previous
                # clean StrategicLog row.
                verdict = await review_read(client, result.content,
                                            surface="log", session=session)
                full_cost = (result.cost_usd or 0.0) + (verdict.cost_usd or 0.0)
                if not verdict.clean:
                    session.add(AICall(
                        model=result.model,
                        prompt_tokens=result.prompt_tokens,
                        completion_tokens=result.completion_tokens,
                        cost_usd=full_cost, status="leaked",
                    ))
                    await session.commit()
                    log.warning("ai_log.reviewer_rejected",
                                tone=tone, analysis=analysis,
                                reason=verdict.reason,
                                preview=result.content[:120])
                    continue

                slog = StrategicLog(
                    generated_at=utcnow(),
                    model=result.model,
                    anchor_date=anchor,
                    prompt_version=PROMPT_VERSION,
                    tone=tone,
                    analysis=analysis,
                    content=result.content,
                    prompt_tokens=result.prompt_tokens,
                    completion_tokens=result.completion_tokens,
                    cost_usd=full_cost,
                    reviewer_score=verdict.score,
                )
                session.add(slog)
                session.add(AICall(
                    model=result.model,
                    prompt_tokens=result.prompt_tokens,
                    completion_tokens=result.completion_tokens,
                    cost_usd=full_cost,
                    status="ok",
                ))
                await session.commit()
                await translate_log_for_active_languages(session, slog.id)
                written += 1
                log.info("ai_log.variant_done",
                         tone=tone, analysis=analysis,
                         prompt_tokens=result.prompt_tokens,
                         completion_tokens=result.completion_tokens)

        jr.items_written = written
        log.info("ai_log.done", variants=written, total=len(variants))


if __name__ == "__main__":
    asyncio.run(run())